{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D2H6YLCPOXvZ",
        "outputId": "395b4a28-c619-4910-fd8d-33def6e7672e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "Collecting datasets\n",
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            "Installing collected packages: xxhash, dill, multiprocess, huggingface-hub, datasets\n",
            "  Attempting uninstall: huggingface-hub\n",
            "    Found existing installation: huggingface-hub 0.20.3\n",
            "    Uninstalling huggingface-hub-0.20.3:\n",
            "      Successfully uninstalled huggingface-hub-0.20.3\n",
            "Successfully installed datasets-2.19.1 dill-0.3.8 huggingface-hub-0.23.0 multiprocess-0.70.16 xxhash-3.4.1\n",
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            "Installing collected packages: nvidia-nvtx-cu12, nvidia-nvjitlink-cu12, nvidia-nccl-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, nvidia-cusparse-cu12, nvidia-cudnn-cu12, nvidia-cusolver-cu12, accelerate\n",
            "Successfully installed accelerate-0.30.1 nvidia-cublas-cu12-12.1.3.1 nvidia-cuda-cupti-cu12-12.1.105 nvidia-cuda-nvrtc-cu12-12.1.105 nvidia-cuda-runtime-cu12-12.1.105 nvidia-cudnn-cu12-8.9.2.26 nvidia-cufft-cu12-11.0.2.54 nvidia-curand-cu12-10.3.2.106 nvidia-cusolver-cu12-11.4.5.107 nvidia-cusparse-cu12-12.1.0.106 nvidia-nccl-cu12-2.19.3 nvidia-nvjitlink-cu12-12.4.127 nvidia-nvtx-cu12-12.1.105\n",
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            "  Downloading evaluate-0.4.2-py3-none-any.whl (84 kB)\n",
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            "Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets>=2.0.0->evaluate) (6.0.5)\n",
            "Requirement already satisfied: yarl<2.0,>=1.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets>=2.0.0->evaluate) (1.9.4)\n",
            "Requirement already satisfied: async-timeout<5.0,>=4.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets>=2.0.0->evaluate) (4.0.3)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.2->pandas->evaluate) (1.16.0)\n",
            "Installing collected packages: evaluate\n",
            "Successfully installed evaluate-0.4.2\n"
          ]
        }
      ],
      "source": [
        "!pip install transformers\n",
        "!pip install datasets\n",
        "!pip install numpy\n",
        "!pip install pandas\n",
        "!pip install accelerate\n",
        "!pip install evaluate"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "H-LANn-hUlZh"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "np.random.seed(11)\n",
        "import torch\n",
        "torch.manual_seed(11)\n",
        "import random\n",
        "random.seed(11)\n",
        "\n",
        "import transformers\n",
        "import pandas as pd\n",
        "\n",
        "from datasets import load_metric\n",
        "from sklearn.metrics import classification_report\n",
        "from sklearn.model_selection import train_test_split\n",
        "from transformers import Trainer\n",
        "from transformers import TrainingArguments\n",
        "from transformers import BertForSequenceClassification\n",
        "from transformers import BertTokenizerFast"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!gdown 1oVUMX0RSMe9nsgiTkrsKX_M-xg5LZbLn # download the train_dev dataset\n",
        "!gdown 1eL0mYZ6jpyAl6rk2IlXaNJnmWnyo4-5E # download the test dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IM--HAJWVJ0D",
        "outputId": "2e1efc99-1813-4b40-b508-08a570ce3f6a"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=1oVUMX0RSMe9nsgiTkrsKX_M-xg5LZbLn\n",
            "To: /content/train_dev.csv\n",
            "100% 1.75M/1.75M [00:00<00:00, 153MB/s]\n",
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=1eL0mYZ6jpyAl6rk2IlXaNJnmWnyo4-5E\n",
            "To: /content/test.csv\n",
            "100% 433k/433k [00:00<00:00, 147MB/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer = BertTokenizerFast.from_pretrained('bert-base-uncased')\n",
        "mlength = 512\n",
        "column = \"anxiety\"\n",
        "\n",
        "train = pd.read_csv(\"/content/train_dev.csv\")\n",
        "test = pd.read_csv(\"/content/test.csv\")\n",
        "train[column] = train[column].astype('float32')\n",
        "test[column] = test[column].astype('float32')"
      ],
      "metadata": {
        "id": "hTL4dINeVkI3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 304,
          "referenced_widgets": [
            "2ae5a238ae5e4332a6e708ae68975944",
            "553c7366f3d248ddb5d29a9305a9157e",
            "f920fa4be4c34a97a8d57e9af514c16f",
            "9aa683331ec8466bb2c8aa21f28a414e",
            "496262e7da8242b8a4f9ddf364931e24",
            "67a44c3c32d346a399d09f5778022773",
            "39c3d8d18c2f47d49ba138f1c82dd30f",
            "e41d03c6c1d64282b18970641f38ece3",
            "f5babe2aaaca4cc8a338dcbb98ec38f6",
            "af734338bac6409c99e627f6b4f7c5e3",
            "0cc8c4ce2f7c437d88b2e55581ff2edb",
            "909e55a6ebf04455a0d7768129d1cf01",
            "e4272cff3afa4f68bf2d2f4f3887e8ef",
            "de7ae21cfdf64a39afac2e28ddb4ad79",
            "79071be716d8454791d9f99a1540b4f5",
            "eb06166e9f1248f28297a4dcc771b866",
            "0f898fb79c54495fbe5710f2db46c213",
            "0799d7a3530442faa805e97fd64d1fd0",
            "160fb9a7cb8f4bbe9a5fa14b6d35f606",
            "aa897ce526064b74ab182a1a299fb0c1",
            "8f602e0d8b2c4b30a19e850b6efdaccb",
            "c4041d177bcd4eafa4572789bbb71063",
            "8a157df8eae84acc88b6a96f5f73fc8b",
            "95f748cc31d54edba976f07d0a8c54fe",
            "f1e3953b8e8146f19aa90855311b01a2",
            "77d8043bb8af4b4c81c4c235a386f7b5",
            "a480426928b2470abff89809c300b147",
            "5229ef33fb6b43d1b898ecad5cb5b226",
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            "6deedb6d0ec84a0cb34d9f5b6e36d2ea"
          ]
        },
        "outputId": "62ab2bc0-465f-43d8-a61b-c55364cb4bc5"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/48.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2ae5a238ae5e4332a6e708ae68975944"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "909e55a6ebf04455a0d7768129d1cf01"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/466k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8a157df8eae84acc88b6a96f5f73fc8b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/570 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d365a4a93caf4aa3b964818e3757736b"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class PsyDataset(torch.utils.data.Dataset):\n",
        "    def __init__(self, encodings, labels):\n",
        "        self.encodings = encodings\n",
        "        self.labels = labels\n",
        "\n",
        "    def __getitem__(self, idx):\n",
        "        item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}\n",
        "        item['labels'] = torch.tensor(self.labels[idx])\n",
        "        return item\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.labels)"
      ],
      "metadata": {
        "id": "s9TlhqaVZmgP"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def train_dev_split(df, seed):\n",
        "  X_train, X_dev, Y_train, Y_dev, indices_train, indices_test = train_test_split(df['text_long'], df[column], df.index, test_size=len(df)//10, random_state = seed)\n",
        "  return [X_train, X_dev, Y_train, Y_dev] # effectively save 10% as validation\n",
        "\n",
        "def data_prep(df, seed = 11):\n",
        "  X_train, X_dev, Y_train, Y_dev = train_dev_split(df, seed)\n",
        "\n",
        "  X_train = list(X_train)\n",
        "  X_dev = list(X_dev)\n",
        "  X_test = list(test[\"text_long\"])\n",
        "\n",
        "  Y_train = list(Y_train)\n",
        "  Y_dev = list(Y_dev)\n",
        "  Y_test = list(test[column])\n",
        "\n",
        "  train_encodings = tokenizer(X_train, truncation=True, padding=True, max_length=mlength)\n",
        "  dev_encodings = tokenizer(X_dev, truncation=True, padding=True, max_length = mlength)\n",
        "  test_encodings = tokenizer(X_test, truncation=True, padding=True, max_length= mlength)\n",
        "\n",
        "  train_dataset = PsyDataset(train_encodings, Y_train)\n",
        "  dev_dataset = PsyDataset(dev_encodings, Y_dev)\n",
        "  test_dataset = PsyDataset(test_encodings, Y_test)\n",
        "  return train_dataset, dev_dataset, test_dataset"
      ],
      "metadata": {
        "id": "JmbJrfELfs9G"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "nRKm_KMi4d5Y"
      },
      "outputs": [],
      "source": [
        "from evaluate import load\n",
        "def compute_metrics(eval_pred):\n",
        "    predictions, labels = eval_pred\n",
        "    metric = load('mse')\n",
        "    return metric.compute(predictions=predictions, references=labels, squared=False)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "training_args = TrainingArguments(\n",
        "  output_dir='./PMethods-results',   # output directory\n",
        "  num_train_epochs=10, # total number of training epochs\n",
        "  per_device_train_batch_size=32,    # batch size per device during training\n",
        "  per_device_eval_batch_size=64,     # batch size for evaluation\n",
        "  warmup_steps=0,                    # number of warmup steps for learning rate scheduler\n",
        "  weight_decay=0.01,                 # strength of weight decay\n",
        "  logging_dir='./logs',              # directory for storing logs\n",
        "  logging_steps=100,\n",
        "  learning_rate = 2e-5,\n",
        "  save_strategy= \"epoch\",\n",
        "  evaluation_strategy=\"epoch\",\n",
        "  load_best_model_at_end= True,\n",
        "  metric_for_best_model=\"mse\",\n",
        "  greater_is_better=False,\n",
        "  seed = 11,\n",
        ")\n",
        "\n",
        "def model_init():\n",
        "    return BertForSequenceClassification.from_pretrained(\"bert-base-uncased\", num_labels=1)"
      ],
      "metadata": {
        "id": "KyOxYddvgFkP"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "_fVi9IbRDSgB",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
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        },
        "outputId": "2fa7f524-a07f-4ca4-8648-5585690fdc37"
      },
      "outputs": [
        {
          "metadata": {
            "tags": null
          },
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "2dcb6b62787c49fc910c9d353816ea12",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/440M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['classifier.bias', 'classifier.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
            "Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['classifier.bias', 'classifier.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='300' max='300' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [300/300 03:28, Epoch 20/20]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Epoch</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "      <th>Mse</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>No log</td>\n",
              "      <td>15.683839</td>\n",
              "      <td>3.960283</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>No log</td>\n",
              "      <td>7.221353</td>\n",
              "      <td>2.687258</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>No log</td>\n",
              "      <td>6.684727</td>\n",
              "      <td>2.585484</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>No log</td>\n",
              "      <td>6.195910</td>\n",
              "      <td>2.489158</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>No log</td>\n",
              "      <td>4.364092</td>\n",
              "      <td>2.089041</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6</td>\n",
              "      <td>No log</td>\n",
              "      <td>4.432302</td>\n",
              "      <td>2.105304</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7</td>\n",
              "      <td>8.960200</td>\n",
              "      <td>4.019575</td>\n",
              "      <td>2.004888</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8</td>\n",
              "      <td>8.960200</td>\n",
              "      <td>3.680809</td>\n",
              "      <td>1.918543</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9</td>\n",
              "      <td>8.960200</td>\n",
              "      <td>3.700473</td>\n",
              "      <td>1.923661</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>10</td>\n",
              "      <td>8.960200</td>\n",
              "      <td>3.561142</td>\n",
              "      <td>1.887099</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>11</td>\n",
              "      <td>8.960200</td>\n",
              "      <td>3.697210</td>\n",
              "      <td>1.922813</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>12</td>\n",
              "      <td>8.960200</td>\n",
              "      <td>3.604553</td>\n",
              "      <td>1.898566</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>13</td>\n",
              "      <td>8.960200</td>\n",
              "      <td>3.657163</td>\n",
              "      <td>1.912371</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>14</td>\n",
              "      <td>0.812700</td>\n",
              "      <td>3.560994</td>\n",
              "      <td>1.887060</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>15</td>\n",
              "      <td>0.812700</td>\n",
              "      <td>3.520543</td>\n",
              "      <td>1.876311</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>16</td>\n",
              "      <td>0.812700</td>\n",
              "      <td>3.558834</td>\n",
              "      <td>1.886487</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>17</td>\n",
              "      <td>0.812700</td>\n",
              "      <td>3.630383</td>\n",
              "      <td>1.905356</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>18</td>\n",
              "      <td>0.812700</td>\n",
              "      <td>3.608886</td>\n",
              "      <td>1.899707</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>19</td>\n",
              "      <td>0.812700</td>\n",
              "      <td>3.588825</td>\n",
              "      <td>1.894420</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>20</td>\n",
              "      <td>0.276500</td>\n",
              "      <td>3.586154</td>\n",
              "      <td>1.893714</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
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            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/4.55k [00:00<?, ?B/s]"
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              "Downloading builder script:   0%|          | 0.00/5.30k [00:00<?, ?B/s]"
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          "text": [
            "500 {'mse': 1.9578266333680654} {'pearsonr': 0.549726685654796} 221.6997423171997\n"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
            "  warnings.warn(\n",
            "Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['classifier.bias', 'classifier.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
            "Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['classifier.bias', 'classifier.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='580' max='580' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [580/580 07:17, Epoch 20/20]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Epoch</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "      <th>Mse</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>No log</td>\n",
              "      <td>6.577473</td>\n",
              "      <td>2.564658</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>No log</td>\n",
              "      <td>4.902045</td>\n",
              "      <td>2.214056</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>No log</td>\n",
              "      <td>4.318521</td>\n",
              "      <td>2.078105</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>9.163200</td>\n",
              "      <td>3.077465</td>\n",
              "      <td>1.754270</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>9.163200</td>\n",
              "      <td>2.983136</td>\n",
              "      <td>1.727176</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6</td>\n",
              "      <td>9.163200</td>\n",
              "      <td>3.047108</td>\n",
              "      <td>1.745597</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7</td>\n",
              "      <td>1.715500</td>\n",
              "      <td>2.673109</td>\n",
              "      <td>1.634964</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8</td>\n",
              "      <td>1.715500</td>\n",
              "      <td>2.806295</td>\n",
              "      <td>1.675200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9</td>\n",
              "      <td>1.715500</td>\n",
              "      <td>2.824419</td>\n",
              "      <td>1.680601</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>10</td>\n",
              "      <td>1.715500</td>\n",
              "      <td>2.616890</td>\n",
              "      <td>1.617680</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>11</td>\n",
              "      <td>0.438200</td>\n",
              "      <td>2.904025</td>\n",
              "      <td>1.704120</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>12</td>\n",
              "      <td>0.438200</td>\n",
              "      <td>3.149219</td>\n",
              "      <td>1.774604</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>13</td>\n",
              "      <td>0.438200</td>\n",
              "      <td>2.800099</td>\n",
              "      <td>1.673350</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>14</td>\n",
              "      <td>0.236400</td>\n",
              "      <td>2.737939</td>\n",
              "      <td>1.654672</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>15</td>\n",
              "      <td>0.236400</td>\n",
              "      <td>2.794371</td>\n",
              "      <td>1.671637</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>16</td>\n",
              "      <td>0.236400</td>\n",
              "      <td>2.824703</td>\n",
              "      <td>1.680685</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>17</td>\n",
              "      <td>0.236400</td>\n",
              "      <td>2.696300</td>\n",
              "      <td>1.642042</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>18</td>\n",
              "      <td>0.148800</td>\n",
              "      <td>2.858340</td>\n",
              "      <td>1.690662</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>19</td>\n",
              "      <td>0.148800</td>\n",
              "      <td>2.757197</td>\n",
              "      <td>1.660481</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>20</td>\n",
              "      <td>0.148800</td>\n",
              "      <td>2.820886</td>\n",
              "      <td>1.679549</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ],
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          },
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "1000 {'mse': 1.9451685759259782} {'pearsonr': 0.5584923565639742} 447.95098304748535\n"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
            "  warnings.warn(\n",
            "Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['classifier.bias', 'classifier.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
            "Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['classifier.bias', 'classifier.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='546' max='1140' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [ 546/1140 06:47 < 07:24, 1.34 it/s, Epoch 9.56/20]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Epoch</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "      <th>Mse</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>No log</td>\n",
              "      <td>8.328673</td>\n",
              "      <td>2.885944</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>15.507600</td>\n",
              "      <td>5.596625</td>\n",
              "      <td>2.365719</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>15.507600</td>\n",
              "      <td>3.967841</td>\n",
              "      <td>1.991944</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>4.499300</td>\n",
              "      <td>3.749196</td>\n",
              "      <td>1.936284</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>4.499300</td>\n",
              "      <td>3.516321</td>\n",
              "      <td>1.875186</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6</td>\n",
              "      <td>2.983500</td>\n",
              "      <td>3.907410</td>\n",
              "      <td>1.976717</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7</td>\n",
              "      <td>2.983500</td>\n",
              "      <td>3.637002</td>\n",
              "      <td>1.907092</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8</td>\n",
              "      <td>2.075900</td>\n",
              "      <td>3.868542</td>\n",
              "      <td>1.966861</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9</td>\n",
              "      <td>1.439600</td>\n",
              "      <td>3.787551</td>\n",
              "      <td>1.946163</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
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              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='1140' max='1140' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [1140/1140 14:16, Epoch 20/20]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Epoch</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "      <th>Mse</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>No log</td>\n",
              "      <td>8.328673</td>\n",
              "      <td>2.885944</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>15.507600</td>\n",
              "      <td>5.596625</td>\n",
              "      <td>2.365719</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>15.507600</td>\n",
              "      <td>3.967841</td>\n",
              "      <td>1.991944</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>4.499300</td>\n",
              "      <td>3.749196</td>\n",
              "      <td>1.936284</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>4.499300</td>\n",
              "      <td>3.516321</td>\n",
              "      <td>1.875186</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6</td>\n",
              "      <td>2.983500</td>\n",
              "      <td>3.907410</td>\n",
              "      <td>1.976717</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7</td>\n",
              "      <td>2.983500</td>\n",
              "      <td>3.637002</td>\n",
              "      <td>1.907092</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8</td>\n",
              "      <td>2.075900</td>\n",
              "      <td>3.868542</td>\n",
              "      <td>1.966861</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9</td>\n",
              "      <td>1.439600</td>\n",
              "      <td>3.787551</td>\n",
              "      <td>1.946163</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>10</td>\n",
              "      <td>1.439600</td>\n",
              "      <td>4.050949</td>\n",
              "      <td>2.012697</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>11</td>\n",
              "      <td>1.094700</td>\n",
              "      <td>4.096795</td>\n",
              "      <td>2.024054</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>12</td>\n",
              "      <td>1.094700</td>\n",
              "      <td>4.040711</td>\n",
              "      <td>2.010152</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>13</td>\n",
              "      <td>0.938700</td>\n",
              "      <td>4.203070</td>\n",
              "      <td>2.050139</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>14</td>\n",
              "      <td>0.938700</td>\n",
              "      <td>4.060550</td>\n",
              "      <td>2.015081</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>15</td>\n",
              "      <td>0.749800</td>\n",
              "      <td>4.273947</td>\n",
              "      <td>2.067352</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>16</td>\n",
              "      <td>0.655600</td>\n",
              "      <td>4.138476</td>\n",
              "      <td>2.034324</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>17</td>\n",
              "      <td>0.655600</td>\n",
              "      <td>4.043314</td>\n",
              "      <td>2.010799</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>18</td>\n",
              "      <td>0.609900</td>\n",
              "      <td>4.164390</td>\n",
              "      <td>2.040684</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>19</td>\n",
              "      <td>0.609900</td>\n",
              "      <td>4.079555</td>\n",
              "      <td>2.019791</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>20</td>\n",
              "      <td>0.567400</td>\n",
              "      <td>4.066495</td>\n",
              "      <td>2.016555</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": []
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2000 {'mse': 1.8460179955656542} {'pearsonr': 0.5893115301357316} 867.3441648483276\n"
          ]
        }
      ],
      "source": [
        "from collections import defaultdict\n",
        "from evaluate import load\n",
        "import time\n",
        "\n",
        "results = defaultdict(list)\n",
        "sizes = [500, 1000, 2000]\n",
        "for size in sizes:\n",
        "  start = time.time()\n",
        "  if size <= 1000:\n",
        "    training_args.num_train_epochs = 20\n",
        "    training_args.learning_rate = 3e-5\n",
        "  else:\n",
        "    training_args.num_train_epochs = 20\n",
        "    training_args.learning_rate = 1e-5\n",
        "  train_dataset, dev_dataset, test_dataset = data_prep(train[:size])\n",
        "  trainer = Trainer(\n",
        "      model_init=model_init,               # the instantiated 🤗 Transformers model to be trained\n",
        "      args=training_args,                  # training arguments, defined above\n",
        "      train_dataset=train_dataset,         # training dataset\n",
        "      eval_dataset=dev_dataset,            # evaluation dataset\n",
        "      compute_metrics=compute_metrics,     # compute_metrics\n",
        "      )\n",
        "  trainer.train()\n",
        "  output = trainer.predict(test_dataset)\n",
        "  mse_metric = load(\"mse\")\n",
        "  pearsonr_metric = load(\"pearsonr\")\n",
        "  m = mse_metric.compute(predictions=output.predictions, references=output.label_ids, squared = False)\n",
        "  p = pearsonr_metric.compute(predictions=output.predictions, references=output.label_ids)\n",
        "  t = time.time() - start\n",
        "  print(size, m, p, t)\n",
        "  results[size].append(m[\"mse\"])\n",
        "  results[size].append(p[\"pearsonr\"])\n",
        "  results[size].append(t)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for key, val in results.items():\n",
        "  print(key, val)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Dd1Bns5A6CZk",
        "outputId": "f49b4911-31ec-40db-8f10-bbf4926e9f11"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "500 [1.9578266333680654, 0.549726685654796, 221.6997423171997]\n",
            "1000 [1.9451685759259782, 0.5584923565639742, 447.95098304748535]\n",
            "2000 [1.8460179955656542, 0.5893115301357316, 867.3441648483276]\n"
          ]
        }
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "machine_shape": "hm",
      "gpuType": "A100"
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU",
    "widgets": {
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